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A New Modification of Fuzzy C-Means via Particle Swarm Optimization for Noisy Image Segmentation

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Artificial Life and Computational Intelligence (ACALCI 2016)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9592))

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Abstract

This paper presents a new clustering-based algorithm for noisy image segmentation. Fuzzy C-Means (FCM), empowered with a new similarity metric, acts as the clustering method. The common Euclidean distance metric in FCM has been modified with information extracted from a local neighboring window surrounding each pixel. Having different local features extracted for each pixel, Particle Swarm Optimization (PSO) is utilized to combine them in a weighting scheme while forming the proposed similarity metric. This allows each feature to contribute to the clustering performance, resulting in more accurate segmentation results in noisy images compared to other state-of-the-art methods.

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Authors and Affiliations

  1. School of Engineering and Computer Science, Victoria University of Wellington, Wellington, New Zealand

    Saeed Mirghasemi, Ramesh Rayudu & Mengjie Zhang

Authors
  1. Saeed Mirghasemi
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  2. Ramesh Rayudu
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  3. Mengjie Zhang
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Corresponding author

Correspondence to Saeed Mirghasemi .

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Editors and Affiliations

  1. Canberra, Aust Capital Terr, Australia

    Tapabrata Ray

  2. University of New South Wales, Canberra, Aust Capital Terr, Australia

    Ruhul Sarker

  3. RMIT University, Melbourne, Victoria, Australia

    Xiaodong Li

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Mirghasemi, S., Rayudu, R., Zhang, M. (2016). A New Modification of Fuzzy C-Means via Particle Swarm Optimization for Noisy Image Segmentation. In: Ray, T., Sarker, R., Li, X. (eds) Artificial Life and Computational Intelligence. ACALCI 2016. Lecture Notes in Computer Science(), vol 9592. Springer, Cham. https://doi.org/10.1007/978-3-319-28270-1_13

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  • DOI: https://doi.org/10.1007/978-3-319-28270-1_13

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-28269-5

  • Online ISBN: 978-3-319-28270-1

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